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source("tianfengRwrappers.R")

# library(future)
# plan("multiprocess",workers = 8)

CA_dataset2 <- readRDS("CA_dataset2.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
human_coronary <- readRDS("human_coronary.rds")
Idents(human_coronary) <- human_coronary$samples
human_coronary <- RenameIdents(human_coronary,'1' = 'sample1','2' = 'sample2','3' = 'sample3','4' = 'sample4')
human_coronary$samples <- Idents(human_coronary)
Idents(human_coronary) <- human_coronary$Classification1
ds2 <- readRDS("ds2.rds")

#sample info

ggsave("dataset2_sampleinfo.svg",plot = umapplot(CA_dataset2, split.by = "sample"), 
       device = svg, width = 25, height = 5)
ggsave("dataset1_sampleinfo.svg",plot = umapplot(CA_dataset1, split.by = "orig.ident"),
       device = svg, width = 15, height = 5)
ggsave("dataset0_sampleinfo.svg",plot = umapplot(human_coronary, split.by = "samples"),
       device = svg, width = 20, height = 5)

附图：所有marker基因表达热图 show 表达量最高的top5

logfc.threshold = 0.5, min.diff.pct = 0.3 pct.1>0.7

dataset2

svg(paste0("CA_dataset2_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset2_markers$gene, CA_dataset2, genes_to_show$gene,"CA_dataset2_supp")
dev.off()

null device 
          1 

dataset1

svg(paste0("CA_dataset1_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset1_markers$gene, CA_dataset1, genes_to_show$gene,"CA_dataset1_supp")
dev.off()

null device 
          1 

dataset0

human_coronary_markers <- FindAllMarkers(human_coronary, logfc.threshold = 0.5, min.diff.pct = 0.3, only.pos = T)

Calculating cluster FB

Calculating cluster Macrophage

Calculating cluster EC

Calculating cluster SMC

Calculating cluster T cell

Calculating cluster B cell

Calculating cluster Neuron

Calculating cluster Plasma

human_coronary_markers <- human_coronary_markers[human_coronary_markers$pct.1>0.7,] %>% group_by(cluster) 

genes_to_show <- human_coronary_markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = avg_logFC)

svg(paste0("human_coronary_supp","_markers.svg"), height = 10, width = 15)
dhm2(human_coronary_markers$gene, human_coronary, genes_to_show$gene,"human_coronary_supp")
dev.off()

RStudioGD 
        2 

样本细胞比例

Idents(human_coronary) <- human_coronary$conditions
sp1 <- subset(human_coronary, idents = "sample1")
sp2 <- subset(human_coronary, idents = "sample2")
sp3 <- subset(human_coronary, idents = "sample3")
sp4 <- subset(human_coronary, idents = "sample4")
prop_mat <- cbind(prop.table(table(sp1$Classification1)),prop.table(table(sp2$Classification1)))
prop_mat2 <- cbind(prop.table(table(sp3$Classification1)),prop.table(table(sp4$Classification1)))
prop_mat <- cbind(prop_mat, prop_mat2)
colnames(prop_mat) <- levels(Idents(human_coronary))


plot_data = melt(prop_mat)
colnames(plot_data) = c('cell type','position','proportion')#修改每一列的名称

ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.5) + theme_bw()

prop_plot <- ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.7) + coord_cartesian(ylim = c(0,0.3))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + scale_fill_manual(values = colors_list[3:6]) +theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 15, colour = "black"),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 15, colour = "black"),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

ggsave("human_coronary_prop.svg", device = svg, plot = prop_plot, width = 12, height = 6)

XGBoost feature

pretrain AC–PA

fea <- read.csv("./datatable/AC_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACpretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PA_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PApretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)

model AC–PA

fea <- read.csv("./datatable/ACtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PAtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PAmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)

model ds2

fea <- read.csv("./datatable/ds2_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2,labels = "",label = F)
ggsave("ds2model_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/ds0_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds0,labels = "",label = F)
ggsave("ds0model_features.png", device = png, plot = ggobj, width = 8, height = 8)

scatter 比较 fig.3 DCN 和 LUM 在 FbM 中 sup vs unsup

ds2 -> ds0

bst_model <- readRDS("ds2_model.rds")
ds2_data <- get_data_table(ds2, highvar = F, type = "data")
Idents(ds2) <- ds2$seurat_clusters
Idents(ds0) <- ds0$seurat_clusters
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(rownames(ds2_data)), ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(rownames(ds2_data),colnames(temp)))
for(i in intersect(rownames(ds2_data), rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
rm(temp)
ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#预测结果
predict_ds0_test <- predict(bst_model, newdata = ds0_test)
predict_prop_ds0 <- matrix(data=predict_ds0_test, nrow = length(levels(Idents(ds2))), 
                           ncol = ncol(ds0), byrow = FALSE, 
                           dimnames = list(levels(Idents(ds2)),colnames(ds0)))

## 得到分群结果
ds0_res <- apply(predict_prop_ds0,2,func,rownames(predict_prop_ds0))
Idents(ds0) <- factor(ds0_res,levels = c(0:4))
umapplot(ds0)

ds0$supclustering <- Idents(ds0) #保存监督聚类结果

ggplot(data, aes(x=LUM, y=DCN, color = group, group = group)) +
  geom_point(size = 3) + 
  geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

Idents(ds2) <- ds2$Classification1
ds2_SMC2 <- subset(ds2, ident = "SMC2")


data2 <- FetchData(object = ds2_SMC2, vars = c("ACTA2", "TAGLN"))
rownames(data2) <-  NULL
data2$group <- "unsup"

ggplot(data2, aes(x=ACTA2, y=TAGLN, color = group, group = group)) +
  geom_point(size = 3,alpha=0.1) + 
  geom_smooth(method=lm , color="red", fill="#69b3a2", formula = 'y~x', se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

data2 <- FetchData(object = ds2_SMC2, vars = c("SOST", "DLX5"))
rownames(data2) <-  NULL
data2$group <- "unsup"

ggplot(data2, aes(x=SOST, y=DLX5, color = group, group = group)) +
  geom_point(size = 3,alpha=0.1) + 
  geom_smooth(method=lm , color="red", fill="#69b3a2", formula = 'y~x', se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

# dim(subset(ds2_SMC2, SOST>0))[2]

dim(subset(ds2_SMC2, `DLX5`>1))[2]

[1] 503

dim(subset(ds2_SMC2, `DLX5`>1&SOST>1))[2]

[1] 261

print("...")

[1] "..."

dim(subset(ds2_SMC2, DLX5>1))[2]

[1] 503

dim(subset(ds2_SMC2, `PRDM6`>1))[2]

[1] 208

dim(subset(ds2_SMC2, `PRDM6`>1&DLX5>1))[2]

[1] 126

dim(subset(ds2_SMC2, ACTA2>1))[2]

[1] 792

dim(subset(ds2_SMC2, TAGLN>1))[2]

[1] 959

dim(subset(ds2_SMC2, TAGLN>1&ACTA2>1))[2]

[1] 781

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